Bloomberg Opinion columnist Gautam Mukunda argues that the recent wave of AI driven layoffs is a strategic mistake that may cost companies more than they save. Published on Nov 10, 2025, Mukunda frames many cuts as short termist moves driven by investor pressure rather than deliberate investments in capability. Firms that substitute headcount for thoughtful retooling risk losing the human judgment and institutional knowledge required to make AI investments pay off.

Background: the problem automation is meant to solve

Automation and AI promise to reduce repetitive work, speed decision making, and lower costs. Yet some firms treat AI adoption as an accounting exercise: deploy models, then immediately cut staff to justify efficiency claims. Mukunda notes the same company names often appear in headlines about both AI investment and large layoffs. The tension is simple:

• Automation succeeds when paired with human expertise that knows how to apply it.
• Mass layoffs remove that expertise, eroding the organizational muscle needed to deploy AI safely and creatively.

Key findings

Mukunda centers his argument on several interlocking costs of firing at scale in the name of AI efficiency:

• Loss of institutional knowledge retention: Employees who understand legacy systems, customer nuances, and edge cases hold context that models alone do not capture.
• Erosion of trust and morale: Rapid downsizing undermines the psychological contract between employer and remaining staff, reducing discretionary effort and increasing turnover risk.
• Reduced adaptability and innovation: Without experienced practitioners to identify new use cases, iterate models responsibly, and integrate AI into workflows, companies risk stalling the very innovation they seek.
• Short termist incentives: Many cuts respond to quarterly pressures rather than strategic choices about capability building.

Context and research

Past forecasts and productivity studies provide useful context. The World Economic Forum predicted that automation will change task composition, displacing some roles while creating others. Research shows automation value compounds when human analysts use machine outputs to drive novel decisions rather than only replace routine work.

Practical implications for leaders

Mukunda does not argue against automation. His caution is about sequencing and strategy. Practical steps for executives include:

• Strategic sequencing matters Do not treat model deployment and headcount reductions as interchangeable. Companies that retain experienced staff to work with AI are better positioned to translate models into sustained business value.
• Account for hidden costs Payroll savings can be offset by slower product development, increased error rates, customer dissatisfaction, and higher rehiring costs when new talent must be sourced externally.
• Invest in upskilling and reskilling Create hybrid roles such as AI integrators, model auditors, and domain expert annotators to preserve context and improve model quality. Building a resilient workforce through upskilling and reskilling programs is a high value approach.
• Strengthen AI governance Responsible AI requires oversight, documentation, and people who understand failure modes. High turnover makes governance brittle and increases the risk of compliance failures or reputational damage.
• Balance investor expectations Boards and executives should align investor governance with a multiyear roadmap for capability building rather than prioritizing short term metrics alone.

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Analysis

This critique aligns with trends we have observed in automation programs. Companies that built internal AI fluency by retaining and redeploying staff tended to see higher return on investment and fewer operational surprises than firms that immediately trimmed teams. Human expertise combined with AI capability often unlocks new value that pure automation cannot.

Conclusion

Mukunda's Bloomberg piece is a timely reminder that AI is not a plug and play replacement for human judgment. Layoffs framed as automation gains may deliver headline savings while hollowing out the capabilities that make AI useful. The prudent path for business leaders is to treat AI as a force multiplier that requires experienced people to steer it. As organizations decide between cost cutting and capability building, the winners will likely be those that invest in people as part of their AI strategy rather than see them as a first line of cost reduction.